EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters.

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Mathematical Psychology

Background:

• Decision-making research often involves continuous outcomes, requiring dynamic theories that balance speed and accuracy.
• The circular diffusion model (CDM) is a key continuous model but is mathematically complex, limiting its use.
• Existing methods for CDM parameter estimation are often intricate and require extensive programming knowledge.

Purpose of the Study:

• To introduce a straightforward, user-friendly method for estimating circular diffusion model (CDM) parameters.
• To enable researchers to fit the CDM to continuous-scale data without advanced programming or theoretical background.
• To demonstrate the method's accuracy, robustness, and applicability to experimental data.

Main Methods:

• Development of simple formulas for CDM parameter estimation and model fitting.
• Comparison of the proposed method's accuracy against maximum likelihood estimation (MLE).
• Introduction of a robust version of the method to handle outlier or contaminant responses.
• Assessment of the method's reliability in measuring CDM parameters with across-trial variability.

Main Results:

• The proposed method achieves accuracy comparable to maximum likelihood estimation.
• A robust version of the method demonstrates high resistance to contaminant responses.
• The approach reliably measures key CDM parameters, even with across-trial variability.
• The method was successfully applied to experimental data, including estimating the probability of guessing.

Conclusions:

• The developed method simplifies CDM parameter estimation and model fitting for continuous response tasks.
• This approach can bridge the gap between theoretical cognitive models and empirical observations.
• The methodology is expected to encourage wider adoption of continuous response paradigms in cognitive psychology research.